1. Field
This disclosure generally relates to classification of consumers. More particularly, the disclosure relates to automatic classification of consumers into micro-segments.
2. General Background
Content providers, merchants, and marketers have to precisely define and target highly specific market segments in order to effectively deliver the most relevant online content. Examples of the most relevant online content are advertising, offers, entertainment, news, etc.
A micro-segment is a precise division of a market or population that is typically identified by marketers through advanced technology and techniques. For example, data mining, artificial intelligence, and various algorithms may be utilized. These technologies and techniques are utilized to group consumers into fine-grained segments by recognizing and predicting minute consumer spending and behavioral patterns, i.e., micro-segmentation. In other words, a micro-segment is a group of (purchase) decision makers who share similar attributes, purchase behavior, and/or level of interest in a specific set of features. In the current environment, however, classifying and segmenting a new user community into micro-segments may be difficult for a number of reasons. In particular, consumers are increasingly filtering content and marketing messages, which reduces marketer efficacy. Further, even as more consumer data and behaviors are collected, most are under-utilized because of the lack of industry expertise and limitations of available technology. In addition, meaningful segmentation within newly created user communities and populations is difficult.
Further, segmentation difficulties also affect numerous websites that leverage the recorded behaviors of large numbers of site users in determining recommended content, products, and services for various user segments. Recommendation systems utilize algorithms that may vary from k-nearest neighborhood approaches to preference/interest/taste similarity methods, e.g., found by using Pearson Correlation, to collaborative filtering algorithms, e.g., people who buy X also buy Y. A challenge with all of these approaches is having an accurate segmentation of very large user populations based on recorded preferences and behaviors before the system can make recommendations.